Accounting for Individual-Specific Heterogeneity in Intergenerational Income Mobility
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| Title: | Accounting for Individual-Specific Heterogeneity in Intergenerational Income Mobility |
|---|---|
| Language: | English |
| Authors: | Yoosoon Chang (ORCID |
| Source: | Sociological Methods & Research. 2025 54(4):1505-1531. |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 27 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Nonparametric Statistics, Social Mobility, Parent Influence, Markov Processes, Race, Educational Attainment, Parent Background, Mothers, Birth, Age, Family Income, Models, Probability |
| Assessment and Survey Identifiers: | Panel Study of Income Dynamics |
| DOI: | 10.1177/00491241251339654 |
| ISSN: | 0049-1241 1552-8294 |
| Abstract: | This article proposes a fully nonparametric model to investigate the dynamics of intergenerational income mobility for discrete outcomes. In our model, an individual's income class probabilities depend on parental income in a manner that accommodates nonlinearities and interactions among various individual and parental characteristics, including race, education, and parental age at childbearing, and so generalizes Markov chain mobility models. We show how the model may be estimated using kernel techniques from machine learning. Utilizing data from the panel study of income dynamics, we show how race, parental education, and mother's age at birth interact with family income to determine mobility between generations. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1485923 |
| Database: | ERIC |
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| Abstract: | This article proposes a fully nonparametric model to investigate the dynamics of intergenerational income mobility for discrete outcomes. In our model, an individual's income class probabilities depend on parental income in a manner that accommodates nonlinearities and interactions among various individual and parental characteristics, including race, education, and parental age at childbearing, and so generalizes Markov chain mobility models. We show how the model may be estimated using kernel techniques from machine learning. Utilizing data from the panel study of income dynamics, we show how race, parental education, and mother's age at birth interact with family income to determine mobility between generations. |
|---|---|
| ISSN: | 0049-1241 1552-8294 |
| DOI: | 10.1177/00491241251339654 |
